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0.2.8 Skip lists

In a paper entitled Skip Lists: A Probabilistic Alternative to Balanced Trees William Pugh at the University of Maryland proposed a linked-list like data structure with some additional features which improved traversal speed. Binary trees work well when the elements which they are to contain are inserted in a random order. As we have seen in the previous sections, BSTs perform very poorly when data is inserted in order. Red-Black trees, which do not suffer from     degenerate performance manifested by BSTs, must constantly rotate and re-balance as data is inserted in order. A skip list is an ordered linked list in which every node contains a variable number of links to other nodes. The nth link of a given node points to subsequent nodes in the list skipping over some number of intermediary nodes. These skipped nodes are common in that they have fewer than n links associated with them.

Because most nodes have a variable number of links, a skip list is really a collection of linked lists of different levels. In order to quickly traverse the structure looking for some target key we search on the upper level list until either the target data is encountered or we find a node with a key smaller than the target which links to a subsequent node with a larget value than the target. In the second case we continue by repeating the same procedure beginning at the node with the smaller value than the target and continuing on the list one level down.

In the following quotation William Pugh describes the performance of his skip list data structure:

Skip lists are a probabilistic alternative to balanced trees. Skip lists are balanced by consulting a random number generator. Although skip lists have bad worst-case performance, no input sequence consistently produces the worst-case performance (much like quicksort when the pivot element is chosen randomly). It is very unlikely a skip list data structure will be significantly unbalanced (e.g., for a dictionary of more than 250 elements, the chance that a search will take more than 3 times the expected time is less than one in a million). Skip lists have balance properties similar to that of search trees built by random insertions, yet do not require insertions to be random.

Balancing a data structure probabilistically is easier than explicitly maintaining the balance. For many applications, skip lists are a more natural representation than trees, also leading to simpler algorithms. The simplicity of skip list algorithms makes them easier to implement and provides significant constant factor speed improvements over balanced tree and self-adjusting tree algorithms. Skip lists are also very space efficient. They can easily be configured to require an average of 1 1/3 pointers per element (or even less) and do not require balance or priority information to be stored with each node.

The above article appeared in the June 1990 issue of the Communications of the ACM. A Postscript format version is available for anonymous ftp from The author, William Pugh, can be contacted at

Scott Gasch